Electrical Distribution Substations contain one or more Substation Transformers, which step down the voltage from high transmission line levels (typically 130 kV to 700 kV) to the medium voltage levels (typically from 4 kV to about 35 kV) at which power is distributed to consumers within a distribution service area. At the edge of the Distribution Grid are a number of Service Transformers, which transform the medium voltage of the distribution grid to the low voltages (in the US, typically 120, 208, 240, 277, or 480) required for commercial, industrial, and residential consumers. Other voltages in addition to some of these can be used elsewhere in the world. Each Service Transformer powers one or more metered loads. A load can be a dwelling, a commercial or industrial building, an element of municipal infrastructure such as a series of street lamps, or agricultural apparatus such as irrigation systems.
Other than the wires connecting a consumer load and the associated meter to a service transformer, the service transformer is the outermost element of the distribution grid before the power is actually delivered to a consumer. A meter is typically attached at the point where the power from the service transformer is delivered to a consumer. Service transformers can be three-phase, dual-phase, or single phase, as can meters. Herein the collection of electrical apparatus inclusive from a service transformer to the collection of at least two communicating electrical meters is referred to as a Transformer Area Network (TAN). A TAN can have a radial topology, such as is common in the US, or it can have a linear or “bus” topology, as is more common in Europe and elsewhere in the world.
Traditionally, reading meters was one of the largest operational costs incurred by electrical utilities. Original electric meters were analog devices with an optical read-out that had to be manually examined monthly to drive the utility billing process. Beginning in the 1970s, mechanisms for digitizing meter data and automating its collection began to be deployed. These mechanisms evolved from walk-by or drive-by systems where the meter would broadcast its current reading using a short-range radio signal, which was received by a device carried by the meter reader. These early systems were known as Automated Meter Reading systems or AMRs. Later, a variety of purpose-built data collection networks, commonly employing a combination of short-range RF repeaters in a mesh configuration with collection points equipped with broadband backhaul means for transporting aggregated readings began to be deployed.
These networks were capable of two-way communication between the “metering head-end” at a utility service center and the meters at the edge of this data collection network, which is generally called an Advanced Metering Infrastructure or AMI. AMIs can collect and store readings frequently, typically as often as every 15 minutes, and can report them nearly that often. They can read any meter on demand provided that this feature is used sparingly, and can connect or disconnect any meter on demand as well. AMI meters can pass signals to consumer devices for the purpose of energy conservation, demand management, and variable-rate billing. Because the AMI network is separate from the power distribution grid except for the intersection at the meters, AMI meters are neither aware of nor sensitive to changes in the grid topology or certain conditions on the grid. Nonetheless, the introduction of AMI is generally considered to be the beginning of the distribution Smart Grid. Additionally, because of the mesh architecture typically used in the AMIs in the United States, the available bandwidth for an individual electrical meter to send its own data is quite limited.
The total billable kilowatt-hours produced by a typical electrical distribution grid anywhere in the world is substantially less than the actual power distributed, as measured at a distribution substation, over the billing period. The loss of power can be classified into two groups. Technical losses result from the overall impedance of the distribution infrastructure, from power-factor mismatch between what the population of loads requires and what the grid produces at each load point, and the fact that utilities oversupply voltage to ensure that power sags will not occur during unpredictable peak loads. Utilities can work to minimize these technical losses, but some technical losses are unavoidable.
Non-technical losses of actual power-hours (as opposed to revenue) result from power theft by consumers who avoid or subvert the metering process by tampering with meters or by tapping power lines above the metered load points. Non-technical revenue losses also include non-payment of bills by customers, and accounting errors by utilities. However, these types of revenue losses are addressed by Meter Data Management systems integrated with the Advanced Metering Infrastructure. These automated systems have the capability to prevent clerical errors, to immediately cut off service to non-paying customers, and to require customers with poor payment histories to be on a pay-in-advance billing plan. Because AMIs provide little or no information about the grid-schematic relationship of one electrical meter to another and the relationship between the electrical meter and the service transformer supplying it with power, AMIs are of little value in pinpointing the source of power theft. Some Smart Meters can detect and report tampering. On the other hand, the absence of meter readers from neighborhoods reduces the chance that illegal taps will be seen and reported.
The social and financial costs of power theft are highly variable, in the developing world, these costs are quite high: sometimes exceeding 50% of power delivered from substations. In India, for example, the major private utilities (Reliance and Tata) report non-technical losses around 10%, but the state-owned utilities have losses exceeding 30% in most cases, according to the India's Maharashtra Electricity Regulation Commission (MERC).
In the developed world, losses from theft represent a relatively small percentage of the total generation cost. In the United States, losses from theft have traditionally been estimated at one to three percent of revenue, though this figure increases during difficult economic times.
Power theft represents a safety and quality service issue as well as an economic issue. Jury-rigging power taps is dangerous and often results in injury and even death. Additionally, the jury-rigged taps represent a fire hazard. Most significantly the resultant unpredictable loading of the distribution grid can cause transformer fires and explosions that can result not only in dangerous situations but in major power outages.
Prior art methods for detecting power theft can be divided into three categories. One category involves comparing voltage and current at a meter with voltage and current at a point of origin for delivery, such as the service distribution transformer for a neighborhood. The technical losses due to the resistance of the low voltage line between the point of origin and each meter are presumed to be less than a predetermined amount, so that any difference in power loss above the predetermined amount can be presumed to be due either to theft or to line defects. United States Patent Application Publication No. 2012/0265355, titled System and Method for Single and and Multizonal Optimization of Utility Services Delivery and Utilization (incorporated herein by reference) describes a system of this sort, wherein intelligent software agents at the service transformer collect measurements both at the transformer and from other instruments located at or incorporated in the electric meters. Theft detection is cited as one of the applications of this system. However, systems involving placing agents and instruments at the transformer are less desirable than would be a system that did not require any devices at the transformer, because transformers are far less physically accessible than meter sockets, and modifying them by adding instrumentation inside the transformer housing or on the high-voltage side of the transformer can be costly and even dangerous.
A second category involves measuring current and voltage outside the meter of a metered load, and inside the premises of the metered load. If more power is being used on the premises than is being delivered via the meter, then either power is being locally generated on the premises, or the meter is being bypassed. Methods of this sort are problematic for utilities because a service utility typically does not have access to data from inside the metered load. The consumer would have to agree to the placement of devices inside the premises.
A third category involves detecting the instantaneous changes in power usage or minor outages caused by tampering with the distribution lines in order to install an unmetered tap. This category of mechanism fails short because tampering can be masked by larger events such as a legitimate outage or interruption in service, and because it would create many false positives.